Cancers figure among the leading causes of death worldwide, accounting for 8.2 million deaths in 2012 (Globocan 2012, IARC). It is expected that annual cancer cases will rise from 14 million in 2012 to 22 million in the next two decades (Globocan 2012, IARC).
As an example, lung cancer causes about 25% of all cancer-related mortality, wherein 80-90% of lung cancers are estimated to be caused by smoking. In light of the poor prognosis and limited treatment options for lung cancer patients at the time of diagnosis, early detection of lung cancer and surgical treatment are key to improving the outlook for lung cancer patients.
At the time of diagnosis, the majority of lung cancer cases present themselves as advanced cancer, often having metastasized to distant regions. Between 50%-60% of small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC) cases are detected at stage IV, while only a small fraction (˜6%) of tumors are detected at early stages, often by chance and because of unrelated symptoms. Lung cancer detected at stage I has a >70% five year survival rate, while the outlook for patients to survive five years following primary diagnosis at stage IV is only at around 2%. These numbers clearly indicate the need for lung cancer early detection programs, as shifting the time of diagnosis to stage I tumors would have an immediate impact on the overall survival rates.
For many years researchers have published studies on serum markers that may have the potential to be used for the diagnosis of lung cancer at early stages. Among these markers autoantibodies that are generated by lung cancer patients against their tumor-associated antigens have been the most promising ones (see references [1], [2], [3], and [4] for examples and a general overview). Nevertheless, until now, no serum or plasma based in vitro diagnostic device (IVDD) has been established or has been widely accepted for the early diagnosis of lung cancer in a screening population. This may be due to the heterogeneity of the disease which impedes an effective diagnosis by a single test.
Another major reason is the low acceptance of diagnostic devices that cause too many false-positives in a screening population. False-positive results cause unnecessary anxiety in the diagnosed individuals and lead to low acceptance by physicians and health care providers because laborious and costly second and third diagnoses are required. The key indicators for physicians and health care providers are the positive and negative predictive value (PPV and NPV, respectively) that define the reliability of a positive or negative test result (dependent on the prevalence of the respective disease in the chosen screening population). A PPV of 20% means that among ten positive test results only two are correct (true-positives), whereas eight are wrong (false-positives) which is usually not acceptable in a screening program for above stated reasons. NPVs are usually high anyway due to the low abundance of a disease in a screening population (e.g. lung cancer at early stages with a prevalence of 1:300 in smoker or approximately 1:36 in smoker >65 years). PPVs below 30-35% are usually unacceptable and not cost-effective. For example, the widely accepted Pap-test for detection of cervical neoplasia has PPVs of >30% (refer to [5] and [6]).
Another example is the U.S. National Lung Screening Trial (NLST; [7]). This study was an 8 year lung cancer screening program comparing low dose computer tomography (LDCT) to chest X-ray in 50,000 smokers (age 50-74). The trial showed 20% reduced mortality in the LDCT cohort but not in the X-ray cohort. However, using LDCT caused 25% of study participants to receive follow-up procedures, which turned out not to be related to lung cancer. Due to this high false-positive rate or low specificity of LDCT, the PPV was ≤4%, meaning that 96 of 100 positive LDCT-results were false-positive. This implies high costs to the health care system when LDCT would be used as a routine screening tool (approximately $240,000 per saved life; [8]) which prevents insurance companies from reimbursing this procedure.
Taken together, PPVs greater than 30% are a prerequisite for the acceptance of a diagnostic device as a screening tool. Some examples of published data of lung cancer screening approaches are shown in Table 1 where specificity and sensitivity of the respective study are listed. PPV and NPV can be calculated for all of these data assuming a target screening population that is at high risk for developing lung cancer. PPVs are significantly below 30% in all cases although a high risk subpopulation (smokers >65 years with a prevalence of lung cancer of 2.8%) was chosen. A second weakness of the data are the relatively low numbers of analyzed samples that do not represent the real screening situation in clinical practice.
TABLE 1Examples of published data of lung cancer screening approachesand corresponding hypothetical PPVs and NPVs.PositiveNegativeNumber of analyzedpredictivepredictiveblood samplesSpeci-Sensi-valuevalueHealthyLungSourceficitytivityPPV*NPV*donorscarcinoma [9]95%55%24%99%6263[10]82%51% 8%98%8585[11]90%47%12%98%5040[12]92%76%22%99%50104[13]89%39% 9%98%23523591%41%12%98%[14]89%36% 9%98%n.a.n.a.*Calculated for a high risk population of lung cancer (smokers > 65 years, prevalence 2.8% according to Robert-Koch-Institute “Cancer in Germany” 2008, published 2012)
The low PPVs calculated from published data are mainly due to the fact that usually specificities ≤95% are achieved (Table 1). The high impact of specificity on the PPV is demonstrated hypothetically in FIG. 1 and Table 2. At a given sensitivity (for example 25%) the PPV can be more than doubled by increasing specificity only by 2 percent points from 97% to 99% (PPV increases from 19.4% to 41.9%). In contrast, sensitivity must be doubled (from 25% to 50%) to have a similar effect on the PPV (increases from 12.6% to 22.4%).
TABLE 2Hypothetical values to demonstrate influence of specificity onPPVs and NPVs.PositiveNegativepredictivepredictivevaluevaluesPrevalence*SpecificitySensitivityPPVNPV2.8%99.0%25.0%41.9%97.9%98.0%25.0%26.5%97.8%97.0%25.0%19.4%97.8%96.0%25.0%15.3%97.8%95.0%25.0%12.6%97.8%95.0%50.0%22.4%98.5%*Calculated for a high risk population of lung cancer (smokers >65 years, prevalence 2.8% according to Robert-Koch-Institute “Cancer in Germany” 2008, published 2012)
If specificity drops below 98%, sensitivity must be greater than 50% to achieve PPVs >30%. This is true even if the risk population (smokers at all ages with a prevalence of 1:300) is further enriched (smokers at age >65 years with a prevalence of 1:36=2.8%). Sensitivities >50% at reasonable specificities are hardly ever achieved in screening programs for heterogeneous diseases like lung cancer.
In conclusion, a specificity of approximately 98-99% is necessary to achieve PPVs >30% for lung cancer screening even for high risk subpopulations. High PPVs in turn are a prerequisite for establishing a diagnostic device used for screening programs as described above. The lower the prevalence of the disease, the higher the specificity must be. Otherwise, the PPV is too low, and too many false-positives cause high costs and low acceptance.
Accordingly, it was an object of the present invention to provide methods and kits for the diagnosis, prognosis and/or monitoring of cancer, in particular lung cancer, in a patient, which facilitate a specificity of at least 97.5%, preferably 98-99%, and a positive predictive value (PPV) of >30%.